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Feedforward neural network
Known as:
Feed-forward neural networks
, Feedforward neural networks
, Feedforward
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A feedforward neural network is an artificial neural network wherein connections between the units do not form a cycle. As such, it is different from…
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Related topics
Related topics
38 relations
Activation function
Artificial intelligence
Artificial neural network
Automatic differentiation
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2010
2010
Overfitting by PSO trained feedforward neural networks
Andrich B. van Wyk
,
A. Engelbrecht
IEEE Congress on Evolutionary Computation
2010
Corpus ID: 42407994
The purpose of this paper is to investigate the overfitting behavior of particle swarm optimization (PSO) trained neural networks…
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2004
2004
A Feedforward Neural Network based on Multi-Valued Neurons
I. Aizenberg
,
C. Moraga
,
D. Paliy
Fuzzy Days
2004
Corpus ID: 10539829
A feedforward neural network based on multi-valued neurons is considered in the paper. It is shown that using a traditional…
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Highly Cited
2001
Highly Cited
2001
Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
S. Haykin
,
J. Lo
,
C. Fancourt
,
J. Príncipe
,
S. Katagiri
2001
Corpus ID: 62000165
From the Publisher: This book deals with a specialized part of neural networks having applications in control, signal processing…
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Highly Cited
2001
Highly Cited
2001
High precision linear motor control via relay-tuning and iterative learning based on zero-phase filtering
K. Tan
,
H. Dou
,
YangQuan Chen
,
Tong-heng Lee
IEEE Transactions on Control Systems Technology
2001
Corpus ID: 16622011
In this paper, with a modest amount of modeling effort, a feedback-feedforward control structure is proposed for precision motion…
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2001
2001
A clustering approach to incremental learning for feedforward neural networks
A. Engelbrecht
,
R. Brits
IJCNN'01. International Joint Conference on…
2001
Corpus ID: 15667557
The sensitivity analysis approach to incremental learning presented by Engelbrecht and Cloete (1999) is extended in this paper…
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Highly Cited
1998
Highly Cited
1998
Establishing impacts of the inputs in a feedforward neural network
T. Tchaban
,
M.J Taylor
,
J. P. Griffin
Neural computing & applications (Print)
1998
Corpus ID: 38216827
Artificial neural network models are now being widely used in various areas of statistical research. Nevertheless, there is a…
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Highly Cited
1996
Highly Cited
1996
A time-domain feedback analysis of filtered-error adaptive gradient algorithms
M. Rupp
,
A. H. Sayed
IEEE Transactions on Signal Processing
1996
Corpus ID: 15295660
This paper provides a time-domain feedback analysis of gradient-based adaptive schemes. A key emphasis is on the robustness…
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Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Radford M. Neal
1995
Corpus ID: 60809283
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions…
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1993
1993
Evolving Neural Feedforward Networks
H. Braun
,
J. Weisbrod
1993
Corpus ID: 59891687
For many practical problem domains the use of neural networks has led to very satisfactory results. Nevertheless the choice of an…
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Highly Cited
1988
Highly Cited
1988
Parallel architectures for artificial neural nets
S. Kung
,
Jenq-Neng Hwang
IEEE International Conference on Neural Networks
1988
Corpus ID: 14186938
The authors advocate digital VLSI architectures for implementing a wide variety of artificial neural nets (ANNs). A programmable…
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